Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f4586aa9390>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f45869d0f28>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function

    real_input = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, (None), name='learning_rate')
    

    return real_input, z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [25]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function

    with tf.variable_scope('discriminator', reuse=reuse):
        alpha = 0.2
        keep_probability=0.9
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        relu1 = tf.maximum(alpha * x1, x1)
        
        d1 = tf.nn.dropout(relu1, keep_probability)
        
        x2 = tf.layers.conv2d(d1, 128, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        d2 = tf.nn.dropout(relu2, keep_probability)
        
        x3 = tf.layers.conv2d(d2, 256, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)


        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [26]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    with tf.variable_scope('generator', reuse=(not is_train)):
        
        alpha = 0.2
        keep_prob = 0.5

        x1 = tf.layers.dense(z, 7*7*512)

        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        #print(x1.shape)
        
        d1 = tf.nn.dropout(x1, keep_prob)
        
        x2 = tf.layers.conv2d_transpose(d1, 256, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        #print(x2.shape)
        
        d2 = tf.nn.dropout(x2, keep_prob)
        
        x3 = tf.layers.conv2d_transpose(d2, 128, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        #print(x3.shape)
        
        d3 = tf.nn.dropout(x3, keep_prob)
    
        x4 = tf.layers.conv2d_transpose(d3, 64, 5, strides=1, padding='same')
        x4 = tf.layers.batch_normalization(x4, training=is_train)
        x4 = tf.maximum(alpha * x4, x4)
        #print(x4.shape)
        
        d4 = tf.nn.dropout(x4, keep_prob)
        
#         x5 = tf.layers.conv2d_transpose(x4, 64, 5, strides=2, padding='valid')
#         x5 = tf.layers.batch_normalization(x5, training=is_train)
#         x5 = tf.maximum(alpha * x5, x5)
#         print(x5.shape)
        
        # Output layer
        logits = tf.layers.conv2d_transpose(d4, out_channel_dim, 5, strides=1, padding='same')
        #print(logits.shape)
        
        out = tf.tanh(logits)
    
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [27]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    
    alpha = 0.02
    smooth = 0.1
    
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=(tf.ones_like(d_model_real) * (1 - smooth))))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [28]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    all_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)

    g_update_ops = [var for var in all_update_ops if var.name.startswith('generator')]
    d_update_ops = [var for var in all_update_ops if var.name.startswith('discriminator')]

    with tf.control_dependencies(d_update_ops):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1 = beta1).minimize(d_loss, var_list = d_vars)

    with tf.control_dependencies(g_update_ops):
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1 = beta1).minimize(g_loss, var_list = g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [29]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [32]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
# TODO: Build Model
    #saver = tf.train.Saver()
    
    sample_z = np.random.uniform(-1, 1, size=(72, z_dim))

    samples, losses = [], []
    steps = 0
    
    #tf.reset_default_graph()
    
    #print(data_shape)
    
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps += 1
                # TODO: Train Model
                batch_images = batch_images.reshape(batch_size, data_shape[1], data_shape[2], data_shape[3])
                batch_images *= 2
                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run optimizers
                _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_train_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})
                _ = sess.run(g_train_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})

                if steps % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))

                if steps % show_every == 0:
                    show_generator_output(sess, 20, input_z, data_shape[3], data_image_mode)

#         saver.save(sess, './checkpoints/generator.ckpt')

#     with open('samples.pkl', 'wb') as f:
#         pkl.dump(samples, f)
    
    show_generator_output(sess, 20, input_z, data_shape[3], data_image_mode)
    
    return losses, samples
                
         

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [33]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5

smooth = 0.1

print_every = 5
show_every = 100



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.7902... Generator Loss: 1.7254
Epoch 1/2... Discriminator Loss: 0.9632... Generator Loss: 2.5739
Epoch 1/2... Discriminator Loss: 1.8868... Generator Loss: 0.3963
Epoch 1/2... Discriminator Loss: 1.1541... Generator Loss: 1.5606
Epoch 1/2... Discriminator Loss: 0.9046... Generator Loss: 1.1660
Epoch 1/2... Discriminator Loss: 1.3856... Generator Loss: 0.7632
Epoch 1/2... Discriminator Loss: 1.5232... Generator Loss: 0.9923
Epoch 1/2... Discriminator Loss: 1.9573... Generator Loss: 0.4841
Epoch 1/2... Discriminator Loss: 1.5855... Generator Loss: 0.6426
Epoch 1/2... Discriminator Loss: 1.5110... Generator Loss: 1.0470
Epoch 1/2... Discriminator Loss: 1.2314... Generator Loss: 0.9283
Epoch 1/2... Discriminator Loss: 1.1745... Generator Loss: 1.2855
Epoch 1/2... Discriminator Loss: 1.7020... Generator Loss: 2.0742
Epoch 1/2... Discriminator Loss: 1.2260... Generator Loss: 1.4685
Epoch 1/2... Discriminator Loss: 1.2896... Generator Loss: 1.5573
Epoch 1/2... Discriminator Loss: 1.1206... Generator Loss: 1.4309
Epoch 1/2... Discriminator Loss: 1.1886... Generator Loss: 1.3421
Epoch 1/2... Discriminator Loss: 1.2593... Generator Loss: 1.0148
Epoch 1/2... Discriminator Loss: 1.8188... Generator Loss: 0.3554
Epoch 1/2... Discriminator Loss: 1.5197... Generator Loss: 0.6480
Epoch 1/2... Discriminator Loss: 1.2154... Generator Loss: 0.7424
Epoch 1/2... Discriminator Loss: 1.1922... Generator Loss: 0.9693
Epoch 1/2... Discriminator Loss: 1.1848... Generator Loss: 1.3075
Epoch 1/2... Discriminator Loss: 1.3775... Generator Loss: 2.0561
Epoch 1/2... Discriminator Loss: 1.2563... Generator Loss: 1.1225
Epoch 1/2... Discriminator Loss: 1.3272... Generator Loss: 1.1569
Epoch 1/2... Discriminator Loss: 1.2547... Generator Loss: 2.0781
Epoch 1/2... Discriminator Loss: 1.1180... Generator Loss: 0.9746
Epoch 1/2... Discriminator Loss: 1.1215... Generator Loss: 1.9920
Epoch 1/2... Discriminator Loss: 1.1860... Generator Loss: 1.0920
Epoch 1/2... Discriminator Loss: 1.0989... Generator Loss: 1.0324
Epoch 1/2... Discriminator Loss: 1.0901... Generator Loss: 1.0383
Epoch 1/2... Discriminator Loss: 1.3229... Generator Loss: 2.0617
Epoch 1/2... Discriminator Loss: 1.1204... Generator Loss: 0.9453
Epoch 1/2... Discriminator Loss: 1.2102... Generator Loss: 0.6694
Epoch 1/2... Discriminator Loss: 1.1033... Generator Loss: 1.3745
Epoch 1/2... Discriminator Loss: 1.0564... Generator Loss: 1.1768
Epoch 1/2... Discriminator Loss: 1.0435... Generator Loss: 1.6432
Epoch 1/2... Discriminator Loss: 1.2693... Generator Loss: 0.6882
Epoch 1/2... Discriminator Loss: 1.1475... Generator Loss: 1.7460
Epoch 1/2... Discriminator Loss: 1.4172... Generator Loss: 0.6449
Epoch 1/2... Discriminator Loss: 1.0098... Generator Loss: 1.2665
Epoch 1/2... Discriminator Loss: 1.0648... Generator Loss: 0.9940
Epoch 1/2... Discriminator Loss: 1.1178... Generator Loss: 1.2851
Epoch 1/2... Discriminator Loss: 1.1214... Generator Loss: 0.9923
Epoch 1/2... Discriminator Loss: 1.0835... Generator Loss: 1.0750
Epoch 1/2... Discriminator Loss: 1.1337... Generator Loss: 1.8968
Epoch 1/2... Discriminator Loss: 1.1228... Generator Loss: 1.2869
Epoch 1/2... Discriminator Loss: 1.1066... Generator Loss: 1.1892
Epoch 1/2... Discriminator Loss: 1.1191... Generator Loss: 1.6638
Epoch 1/2... Discriminator Loss: 1.0922... Generator Loss: 0.9842
Epoch 1/2... Discriminator Loss: 1.0766... Generator Loss: 1.3337
Epoch 1/2... Discriminator Loss: 1.0997... Generator Loss: 1.0117
Epoch 1/2... Discriminator Loss: 1.2001... Generator Loss: 0.7250
Epoch 1/2... Discriminator Loss: 0.9622... Generator Loss: 1.4013
Epoch 1/2... Discriminator Loss: 1.3287... Generator Loss: 0.7281
Epoch 1/2... Discriminator Loss: 1.2103... Generator Loss: 0.7570
Epoch 1/2... Discriminator Loss: 0.9936... Generator Loss: 1.2285
Epoch 1/2... Discriminator Loss: 0.9656... Generator Loss: 1.1342
Epoch 1/2... Discriminator Loss: 1.2502... Generator Loss: 0.7357
Epoch 1/2... Discriminator Loss: 1.1460... Generator Loss: 1.4426
Epoch 1/2... Discriminator Loss: 1.1854... Generator Loss: 0.9029
Epoch 1/2... Discriminator Loss: 1.2070... Generator Loss: 0.8219
Epoch 1/2... Discriminator Loss: 1.0574... Generator Loss: 1.2389
Epoch 1/2... Discriminator Loss: 1.0853... Generator Loss: 1.2164
Epoch 1/2... Discriminator Loss: 1.1746... Generator Loss: 1.4091
Epoch 1/2... Discriminator Loss: 1.1501... Generator Loss: 0.8988
Epoch 1/2... Discriminator Loss: 1.1389... Generator Loss: 0.9465
Epoch 1/2... Discriminator Loss: 1.1250... Generator Loss: 1.1110
Epoch 1/2... Discriminator Loss: 1.0686... Generator Loss: 0.9526
Epoch 1/2... Discriminator Loss: 1.0946... Generator Loss: 1.5615
Epoch 1/2... Discriminator Loss: 1.2054... Generator Loss: 0.9912
Epoch 1/2... Discriminator Loss: 1.1367... Generator Loss: 1.1481
Epoch 1/2... Discriminator Loss: 1.3455... Generator Loss: 1.7931
Epoch 1/2... Discriminator Loss: 1.2680... Generator Loss: 1.7887
Epoch 1/2... Discriminator Loss: 1.3072... Generator Loss: 1.7263
Epoch 1/2... Discriminator Loss: 1.2025... Generator Loss: 1.4003
Epoch 1/2... Discriminator Loss: 1.1328... Generator Loss: 1.0522
Epoch 1/2... Discriminator Loss: 1.0805... Generator Loss: 1.0193
Epoch 1/2... Discriminator Loss: 1.0599... Generator Loss: 1.1031
Epoch 1/2... Discriminator Loss: 1.0585... Generator Loss: 1.0327
Epoch 1/2... Discriminator Loss: 1.1561... Generator Loss: 0.9968
Epoch 1/2... Discriminator Loss: 1.1593... Generator Loss: 0.9841
Epoch 1/2... Discriminator Loss: 1.2481... Generator Loss: 0.8297
Epoch 1/2... Discriminator Loss: 1.0715... Generator Loss: 1.0200
Epoch 1/2... Discriminator Loss: 1.1664... Generator Loss: 1.5916
Epoch 1/2... Discriminator Loss: 1.2043... Generator Loss: 0.8376
Epoch 1/2... Discriminator Loss: 1.2365... Generator Loss: 0.7381
Epoch 1/2... Discriminator Loss: 1.2275... Generator Loss: 1.0231
Epoch 1/2... Discriminator Loss: 1.1153... Generator Loss: 1.1615
Epoch 1/2... Discriminator Loss: 1.2239... Generator Loss: 1.1828
Epoch 1/2... Discriminator Loss: 1.1461... Generator Loss: 0.9943
Epoch 1/2... Discriminator Loss: 1.1848... Generator Loss: 1.2872
Epoch 1/2... Discriminator Loss: 1.1443... Generator Loss: 1.4961
Epoch 1/2... Discriminator Loss: 1.0941... Generator Loss: 1.0793
Epoch 1/2... Discriminator Loss: 1.1981... Generator Loss: 1.4666
Epoch 1/2... Discriminator Loss: 1.1782... Generator Loss: 0.8309
Epoch 1/2... Discriminator Loss: 1.1729... Generator Loss: 0.7669
Epoch 1/2... Discriminator Loss: 1.1339... Generator Loss: 0.8484
Epoch 1/2... Discriminator Loss: 1.2044... Generator Loss: 0.9005
Epoch 1/2... Discriminator Loss: 1.2093... Generator Loss: 1.1562
Epoch 1/2... Discriminator Loss: 1.1297... Generator Loss: 1.3823
Epoch 1/2... Discriminator Loss: 1.1496... Generator Loss: 0.9559
Epoch 1/2... Discriminator Loss: 1.0647... Generator Loss: 0.9273
Epoch 1/2... Discriminator Loss: 1.1050... Generator Loss: 0.9748
Epoch 1/2... Discriminator Loss: 1.0843... Generator Loss: 1.1913
Epoch 1/2... Discriminator Loss: 1.1491... Generator Loss: 1.3978
Epoch 1/2... Discriminator Loss: 1.0619... Generator Loss: 1.1314
Epoch 1/2... Discriminator Loss: 1.2262... Generator Loss: 0.8246
Epoch 1/2... Discriminator Loss: 1.1727... Generator Loss: 0.9320
Epoch 1/2... Discriminator Loss: 1.0878... Generator Loss: 1.0307
Epoch 1/2... Discriminator Loss: 1.0943... Generator Loss: 1.0938
Epoch 1/2... Discriminator Loss: 1.0358... Generator Loss: 1.5423
Epoch 1/2... Discriminator Loss: 1.1299... Generator Loss: 1.2897
Epoch 1/2... Discriminator Loss: 1.0915... Generator Loss: 0.8884
Epoch 1/2... Discriminator Loss: 1.1003... Generator Loss: 1.3469
Epoch 1/2... Discriminator Loss: 1.1217... Generator Loss: 0.8558
Epoch 1/2... Discriminator Loss: 1.1314... Generator Loss: 0.9810
Epoch 1/2... Discriminator Loss: 1.1560... Generator Loss: 0.8869
Epoch 1/2... Discriminator Loss: 1.0763... Generator Loss: 0.9661
Epoch 1/2... Discriminator Loss: 1.1257... Generator Loss: 1.0091
Epoch 1/2... Discriminator Loss: 1.3630... Generator Loss: 0.7445
Epoch 1/2... Discriminator Loss: 1.1051... Generator Loss: 0.8143
Epoch 1/2... Discriminator Loss: 1.1383... Generator Loss: 0.9608
Epoch 1/2... Discriminator Loss: 1.1397... Generator Loss: 1.1864
Epoch 1/2... Discriminator Loss: 1.0364... Generator Loss: 1.1792
Epoch 1/2... Discriminator Loss: 1.0895... Generator Loss: 0.9993
Epoch 1/2... Discriminator Loss: 1.1383... Generator Loss: 1.1071
Epoch 1/2... Discriminator Loss: 1.1683... Generator Loss: 0.8464
Epoch 1/2... Discriminator Loss: 1.2254... Generator Loss: 0.6974
Epoch 1/2... Discriminator Loss: 1.2153... Generator Loss: 1.7024
Epoch 1/2... Discriminator Loss: 1.1739... Generator Loss: 1.0405
Epoch 1/2... Discriminator Loss: 1.1201... Generator Loss: 1.0096
Epoch 1/2... Discriminator Loss: 1.1432... Generator Loss: 0.8171
Epoch 1/2... Discriminator Loss: 1.1023... Generator Loss: 1.1578
Epoch 1/2... Discriminator Loss: 1.1396... Generator Loss: 0.9517
Epoch 1/2... Discriminator Loss: 1.1572... Generator Loss: 0.8029
Epoch 1/2... Discriminator Loss: 1.0922... Generator Loss: 0.9851
Epoch 1/2... Discriminator Loss: 1.1322... Generator Loss: 1.7087
Epoch 1/2... Discriminator Loss: 1.0449... Generator Loss: 1.3968
Epoch 1/2... Discriminator Loss: 1.1459... Generator Loss: 1.0277
Epoch 1/2... Discriminator Loss: 1.0995... Generator Loss: 1.1169
Epoch 1/2... Discriminator Loss: 1.0274... Generator Loss: 1.0883
Epoch 1/2... Discriminator Loss: 1.1775... Generator Loss: 0.9159
Epoch 1/2... Discriminator Loss: 1.1456... Generator Loss: 1.1516
Epoch 1/2... Discriminator Loss: 1.1042... Generator Loss: 0.9432
Epoch 1/2... Discriminator Loss: 1.1618... Generator Loss: 1.0683
Epoch 1/2... Discriminator Loss: 1.2131... Generator Loss: 1.1794
Epoch 1/2... Discriminator Loss: 1.1705... Generator Loss: 0.8716
Epoch 1/2... Discriminator Loss: 1.1536... Generator Loss: 0.8531
Epoch 1/2... Discriminator Loss: 1.2372... Generator Loss: 1.4156
Epoch 1/2... Discriminator Loss: 1.0680... Generator Loss: 1.2269
Epoch 1/2... Discriminator Loss: 1.0758... Generator Loss: 0.9551
Epoch 1/2... Discriminator Loss: 1.1215... Generator Loss: 1.4055
Epoch 1/2... Discriminator Loss: 1.1682... Generator Loss: 0.9773
Epoch 1/2... Discriminator Loss: 1.0359... Generator Loss: 1.2219
Epoch 1/2... Discriminator Loss: 1.2144... Generator Loss: 0.8331
Epoch 1/2... Discriminator Loss: 1.2941... Generator Loss: 1.8574
Epoch 1/2... Discriminator Loss: 1.0549... Generator Loss: 1.1999
Epoch 1/2... Discriminator Loss: 1.0779... Generator Loss: 0.9565
Epoch 1/2... Discriminator Loss: 1.1151... Generator Loss: 1.0083
Epoch 1/2... Discriminator Loss: 1.2156... Generator Loss: 0.7745
Epoch 1/2... Discriminator Loss: 1.1429... Generator Loss: 1.0191
Epoch 1/2... Discriminator Loss: 1.1072... Generator Loss: 1.6126
Epoch 1/2... Discriminator Loss: 1.2399... Generator Loss: 1.0445
Epoch 1/2... Discriminator Loss: 1.0914... Generator Loss: 1.4392
Epoch 1/2... Discriminator Loss: 1.0033... Generator Loss: 1.3386
Epoch 1/2... Discriminator Loss: 1.1467... Generator Loss: 1.1770
Epoch 1/2... Discriminator Loss: 1.1836... Generator Loss: 1.3521
Epoch 1/2... Discriminator Loss: 1.1935... Generator Loss: 1.1146
Epoch 1/2... Discriminator Loss: 1.1774... Generator Loss: 0.8995
Epoch 1/2... Discriminator Loss: 1.1249... Generator Loss: 1.0490
Epoch 1/2... Discriminator Loss: 1.2201... Generator Loss: 1.6229
Epoch 1/2... Discriminator Loss: 1.0555... Generator Loss: 1.0257
Epoch 1/2... Discriminator Loss: 1.1247... Generator Loss: 1.3037
Epoch 1/2... Discriminator Loss: 1.0598... Generator Loss: 1.1107
Epoch 1/2... Discriminator Loss: 1.1089... Generator Loss: 1.2501
Epoch 1/2... Discriminator Loss: 1.0186... Generator Loss: 1.2824
Epoch 1/2... Discriminator Loss: 1.1614... Generator Loss: 0.8940
Epoch 1/2... Discriminator Loss: 1.1455... Generator Loss: 1.1171
Epoch 1/2... Discriminator Loss: 1.0817... Generator Loss: 1.1882
Epoch 1/2... Discriminator Loss: 1.1884... Generator Loss: 0.8449
Epoch 1/2... Discriminator Loss: 1.2355... Generator Loss: 0.7398
Epoch 1/2... Discriminator Loss: 1.1050... Generator Loss: 1.2855
Epoch 1/2... Discriminator Loss: 1.1265... Generator Loss: 0.8325
Epoch 1/2... Discriminator Loss: 1.0397... Generator Loss: 0.9813
Epoch 1/2... Discriminator Loss: 1.0257... Generator Loss: 1.0454
Epoch 2/2... Discriminator Loss: 1.0476... Generator Loss: 1.6257
Epoch 2/2... Discriminator Loss: 1.1686... Generator Loss: 1.0039
Epoch 2/2... Discriminator Loss: 1.1345... Generator Loss: 1.3930
Epoch 2/2... Discriminator Loss: 1.1014... Generator Loss: 1.2929
Epoch 2/2... Discriminator Loss: 0.9789... Generator Loss: 1.2812
Epoch 2/2... Discriminator Loss: 1.1167... Generator Loss: 1.0567
Epoch 2/2... Discriminator Loss: 1.0679... Generator Loss: 0.9781
Epoch 2/2... Discriminator Loss: 1.1223... Generator Loss: 1.1918
Epoch 2/2... Discriminator Loss: 1.2658... Generator Loss: 1.7413
Epoch 2/2... Discriminator Loss: 1.0240... Generator Loss: 1.3667
Epoch 2/2... Discriminator Loss: 0.9610... Generator Loss: 1.3903
Epoch 2/2... Discriminator Loss: 1.2289... Generator Loss: 1.5695
Epoch 2/2... Discriminator Loss: 1.2100... Generator Loss: 1.3239
Epoch 2/2... Discriminator Loss: 1.1104... Generator Loss: 1.5612
Epoch 2/2... Discriminator Loss: 1.0069... Generator Loss: 1.2386
Epoch 2/2... Discriminator Loss: 1.1663... Generator Loss: 0.9732
Epoch 2/2... Discriminator Loss: 1.0165... Generator Loss: 1.5957
Epoch 2/2... Discriminator Loss: 1.1831... Generator Loss: 1.5853
Epoch 2/2... Discriminator Loss: 1.0992... Generator Loss: 1.5041
Epoch 2/2... Discriminator Loss: 1.0464... Generator Loss: 1.3034
Epoch 2/2... Discriminator Loss: 1.0980... Generator Loss: 1.0780
Epoch 2/2... Discriminator Loss: 1.0104... Generator Loss: 1.6742
Epoch 2/2... Discriminator Loss: 1.0882... Generator Loss: 1.2167
Epoch 2/2... Discriminator Loss: 1.1772... Generator Loss: 1.4174
Epoch 2/2... Discriminator Loss: 1.1015... Generator Loss: 1.0839
Epoch 2/2... Discriminator Loss: 1.0563... Generator Loss: 0.8643
Epoch 2/2... Discriminator Loss: 1.1289... Generator Loss: 0.9209
Epoch 2/2... Discriminator Loss: 1.1122... Generator Loss: 1.0537
Epoch 2/2... Discriminator Loss: 1.0153... Generator Loss: 1.2030
Epoch 2/2... Discriminator Loss: 1.0305... Generator Loss: 1.4169
Epoch 2/2... Discriminator Loss: 1.0959... Generator Loss: 1.5708
Epoch 2/2... Discriminator Loss: 1.1219... Generator Loss: 0.9725
Epoch 2/2... Discriminator Loss: 1.3610... Generator Loss: 0.5703
Epoch 2/2... Discriminator Loss: 1.0973... Generator Loss: 1.2265
Epoch 2/2... Discriminator Loss: 1.1039... Generator Loss: 1.3750
Epoch 2/2... Discriminator Loss: 1.0062... Generator Loss: 1.2137
Epoch 2/2... Discriminator Loss: 1.1705... Generator Loss: 1.6844
Epoch 2/2... Discriminator Loss: 1.0671... Generator Loss: 1.3924
Epoch 2/2... Discriminator Loss: 1.1450... Generator Loss: 1.2428
Epoch 2/2... Discriminator Loss: 1.1071... Generator Loss: 1.2720
Epoch 2/2... Discriminator Loss: 1.1326... Generator Loss: 0.8117
Epoch 2/2... Discriminator Loss: 1.0901... Generator Loss: 0.9439
Epoch 2/2... Discriminator Loss: 1.1140... Generator Loss: 0.9503
Epoch 2/2... Discriminator Loss: 0.9822... Generator Loss: 0.9917
Epoch 2/2... Discriminator Loss: 1.0803... Generator Loss: 0.9234
Epoch 2/2... Discriminator Loss: 1.1317... Generator Loss: 1.5485
Epoch 2/2... Discriminator Loss: 1.3369... Generator Loss: 2.1447
Epoch 2/2... Discriminator Loss: 1.1327... Generator Loss: 1.1981
Epoch 2/2... Discriminator Loss: 1.1053... Generator Loss: 0.9453
Epoch 2/2... Discriminator Loss: 1.0257... Generator Loss: 1.2499
Epoch 2/2... Discriminator Loss: 1.0618... Generator Loss: 1.0255
Epoch 2/2... Discriminator Loss: 1.0624... Generator Loss: 1.3696
Epoch 2/2... Discriminator Loss: 1.1388... Generator Loss: 0.8641
Epoch 2/2... Discriminator Loss: 1.2234... Generator Loss: 0.8097
Epoch 2/2... Discriminator Loss: 1.0203... Generator Loss: 1.1438
Epoch 2/2... Discriminator Loss: 1.0409... Generator Loss: 1.1396
Epoch 2/2... Discriminator Loss: 1.0289... Generator Loss: 1.1800
Epoch 2/2... Discriminator Loss: 1.0415... Generator Loss: 1.1294
Epoch 2/2... Discriminator Loss: 1.1450... Generator Loss: 0.9757
Epoch 2/2... Discriminator Loss: 1.0716... Generator Loss: 0.8726
Epoch 2/2... Discriminator Loss: 1.0596... Generator Loss: 1.0637
Epoch 2/2... Discriminator Loss: 1.1341... Generator Loss: 1.6233
Epoch 2/2... Discriminator Loss: 1.0595... Generator Loss: 1.4165
Epoch 2/2... Discriminator Loss: 1.0301... Generator Loss: 1.0737
Epoch 2/2... Discriminator Loss: 1.1965... Generator Loss: 0.8580
Epoch 2/2... Discriminator Loss: 1.1007... Generator Loss: 0.9390
Epoch 2/2... Discriminator Loss: 1.1236... Generator Loss: 1.3763
Epoch 2/2... Discriminator Loss: 1.0315... Generator Loss: 1.0584
Epoch 2/2... Discriminator Loss: 1.0178... Generator Loss: 0.9725
Epoch 2/2... Discriminator Loss: 1.2023... Generator Loss: 0.7324
Epoch 2/2... Discriminator Loss: 0.9492... Generator Loss: 1.2327
Epoch 2/2... Discriminator Loss: 1.1360... Generator Loss: 1.4863
Epoch 2/2... Discriminator Loss: 1.1061... Generator Loss: 1.2352
Epoch 2/2... Discriminator Loss: 1.1417... Generator Loss: 0.8869
Epoch 2/2... Discriminator Loss: 1.0717... Generator Loss: 0.8441
Epoch 2/2... Discriminator Loss: 1.1334... Generator Loss: 0.9072
Epoch 2/2... Discriminator Loss: 1.1435... Generator Loss: 1.0732
Epoch 2/2... Discriminator Loss: 1.1218... Generator Loss: 1.1613
Epoch 2/2... Discriminator Loss: 1.1418... Generator Loss: 1.4386
Epoch 2/2... Discriminator Loss: 1.0189... Generator Loss: 0.9503
Epoch 2/2... Discriminator Loss: 1.2219... Generator Loss: 0.9388
Epoch 2/2... Discriminator Loss: 1.0558... Generator Loss: 0.8424
Epoch 2/2... Discriminator Loss: 1.0964... Generator Loss: 1.1447
Epoch 2/2... Discriminator Loss: 1.0846... Generator Loss: 1.1067
Epoch 2/2... Discriminator Loss: 1.1166... Generator Loss: 1.0237
Epoch 2/2... Discriminator Loss: 1.0743... Generator Loss: 1.2501
Epoch 2/2... Discriminator Loss: 0.9908... Generator Loss: 1.2670
Epoch 2/2... Discriminator Loss: 1.1484... Generator Loss: 1.1937
Epoch 2/2... Discriminator Loss: 1.0325... Generator Loss: 1.3457
Epoch 2/2... Discriminator Loss: 1.0985... Generator Loss: 0.9253
Epoch 2/2... Discriminator Loss: 1.0906... Generator Loss: 1.0383
Epoch 2/2... Discriminator Loss: 1.1049... Generator Loss: 1.4758
Epoch 2/2... Discriminator Loss: 1.0926... Generator Loss: 1.6160
Epoch 2/2... Discriminator Loss: 1.3211... Generator Loss: 0.6514
Epoch 2/2... Discriminator Loss: 1.0470... Generator Loss: 0.8632
Epoch 2/2... Discriminator Loss: 1.0613... Generator Loss: 1.1073
Epoch 2/2... Discriminator Loss: 1.2731... Generator Loss: 1.0436
Epoch 2/2... Discriminator Loss: 1.0535... Generator Loss: 0.9359
Epoch 2/2... Discriminator Loss: 0.9868... Generator Loss: 1.4090
Epoch 2/2... Discriminator Loss: 1.1508... Generator Loss: 0.9007
Epoch 2/2... Discriminator Loss: 1.1142... Generator Loss: 1.4191
Epoch 2/2... Discriminator Loss: 1.0747... Generator Loss: 1.0585
Epoch 2/2... Discriminator Loss: 1.1973... Generator Loss: 0.9631
Epoch 2/2... Discriminator Loss: 1.1743... Generator Loss: 1.3877
Epoch 2/2... Discriminator Loss: 1.0681... Generator Loss: 1.5700
Epoch 2/2... Discriminator Loss: 1.0116... Generator Loss: 1.1408
Epoch 2/2... Discriminator Loss: 1.0078... Generator Loss: 0.9532
Epoch 2/2... Discriminator Loss: 1.1712... Generator Loss: 0.7848
Epoch 2/2... Discriminator Loss: 1.0903... Generator Loss: 0.9196
Epoch 2/2... Discriminator Loss: 1.1289... Generator Loss: 0.8673
Epoch 2/2... Discriminator Loss: 1.0216... Generator Loss: 1.3096
Epoch 2/2... Discriminator Loss: 1.0793... Generator Loss: 1.0327
Epoch 2/2... Discriminator Loss: 1.1765... Generator Loss: 0.9727
Epoch 2/2... Discriminator Loss: 1.0920... Generator Loss: 1.3316
Epoch 2/2... Discriminator Loss: 1.3981... Generator Loss: 0.5876
Epoch 2/2... Discriminator Loss: 1.1691... Generator Loss: 0.8900
Epoch 2/2... Discriminator Loss: 1.1106... Generator Loss: 1.0675
Epoch 2/2... Discriminator Loss: 1.0951... Generator Loss: 1.0197
Epoch 2/2... Discriminator Loss: 1.0263... Generator Loss: 1.3363
Epoch 2/2... Discriminator Loss: 1.0279... Generator Loss: 0.9684
Epoch 2/2... Discriminator Loss: 1.0193... Generator Loss: 0.9678
Epoch 2/2... Discriminator Loss: 1.0841... Generator Loss: 0.8816
Epoch 2/2... Discriminator Loss: 1.1225... Generator Loss: 0.8572
Epoch 2/2... Discriminator Loss: 1.1065... Generator Loss: 1.2700
Epoch 2/2... Discriminator Loss: 1.1221... Generator Loss: 0.8945
Epoch 2/2... Discriminator Loss: 0.9455... Generator Loss: 1.4612
Epoch 2/2... Discriminator Loss: 1.0245... Generator Loss: 1.1618
Epoch 2/2... Discriminator Loss: 0.9421... Generator Loss: 1.1033
Epoch 2/2... Discriminator Loss: 1.0687... Generator Loss: 0.9110
Epoch 2/2... Discriminator Loss: 1.0083... Generator Loss: 0.9271
Epoch 2/2... Discriminator Loss: 1.0828... Generator Loss: 1.2903
Epoch 2/2... Discriminator Loss: 1.0503... Generator Loss: 1.3291
Epoch 2/2... Discriminator Loss: 1.0450... Generator Loss: 1.0509
Epoch 2/2... Discriminator Loss: 1.0217... Generator Loss: 1.2032
Epoch 2/2... Discriminator Loss: 1.0590... Generator Loss: 0.8531
Epoch 2/2... Discriminator Loss: 1.0063... Generator Loss: 1.1993
Epoch 2/2... Discriminator Loss: 1.0389... Generator Loss: 1.2726
Epoch 2/2... Discriminator Loss: 1.2356... Generator Loss: 0.7373
Epoch 2/2... Discriminator Loss: 1.0406... Generator Loss: 1.2942
Epoch 2/2... Discriminator Loss: 1.0478... Generator Loss: 1.1828
Epoch 2/2... Discriminator Loss: 0.9701... Generator Loss: 1.2998
Epoch 2/2... Discriminator Loss: 1.0670... Generator Loss: 0.9793
Epoch 2/2... Discriminator Loss: 1.0589... Generator Loss: 0.9297
Epoch 2/2... Discriminator Loss: 0.9463... Generator Loss: 1.5211
Epoch 2/2... Discriminator Loss: 1.0210... Generator Loss: 1.1704
Epoch 2/2... Discriminator Loss: 1.0903... Generator Loss: 1.4337
Epoch 2/2... Discriminator Loss: 1.0605... Generator Loss: 1.0617
Epoch 2/2... Discriminator Loss: 1.1319... Generator Loss: 1.1522
Epoch 2/2... Discriminator Loss: 1.2881... Generator Loss: 1.6315
Epoch 2/2... Discriminator Loss: 1.1084... Generator Loss: 0.9029
Epoch 2/2... Discriminator Loss: 1.2299... Generator Loss: 1.5223
Epoch 2/2... Discriminator Loss: 1.1638... Generator Loss: 1.5310
Epoch 2/2... Discriminator Loss: 0.9486... Generator Loss: 1.1845
Epoch 2/2... Discriminator Loss: 0.9429... Generator Loss: 1.0119
Epoch 2/2... Discriminator Loss: 1.0854... Generator Loss: 0.9711
Epoch 2/2... Discriminator Loss: 1.1372... Generator Loss: 0.8868
Epoch 2/2... Discriminator Loss: 1.0712... Generator Loss: 1.1837
Epoch 2/2... Discriminator Loss: 1.0901... Generator Loss: 1.3393
Epoch 2/2... Discriminator Loss: 1.0562... Generator Loss: 0.8401
Epoch 2/2... Discriminator Loss: 1.2239... Generator Loss: 0.8457
Epoch 2/2... Discriminator Loss: 1.1737... Generator Loss: 1.4200
Epoch 2/2... Discriminator Loss: 0.8959... Generator Loss: 1.2714
Epoch 2/2... Discriminator Loss: 1.3082... Generator Loss: 0.6430
Epoch 2/2... Discriminator Loss: 1.0080... Generator Loss: 1.0863
Epoch 2/2... Discriminator Loss: 1.1029... Generator Loss: 1.1074
Epoch 2/2... Discriminator Loss: 0.9423... Generator Loss: 1.2151
Epoch 2/2... Discriminator Loss: 1.0412... Generator Loss: 1.2743
Epoch 2/2... Discriminator Loss: 1.0422... Generator Loss: 1.3209
Epoch 2/2... Discriminator Loss: 1.0487... Generator Loss: 0.9460
Epoch 2/2... Discriminator Loss: 1.0539... Generator Loss: 0.8620
Epoch 2/2... Discriminator Loss: 1.0875... Generator Loss: 0.9239
Epoch 2/2... Discriminator Loss: 1.1761... Generator Loss: 0.9921
Epoch 2/2... Discriminator Loss: 1.1476... Generator Loss: 1.1098
Epoch 2/2... Discriminator Loss: 1.0279... Generator Loss: 1.0813
Epoch 2/2... Discriminator Loss: 1.1128... Generator Loss: 0.8965
Epoch 2/2... Discriminator Loss: 1.0823... Generator Loss: 0.8417
Epoch 2/2... Discriminator Loss: 1.0335... Generator Loss: 1.1232
Epoch 2/2... Discriminator Loss: 1.6992... Generator Loss: 0.3767
Epoch 2/2... Discriminator Loss: 1.5076... Generator Loss: 0.6174
Epoch 2/2... Discriminator Loss: 1.1771... Generator Loss: 0.9967
Epoch 2/2... Discriminator Loss: 1.0208... Generator Loss: 1.2201
Epoch 2/2... Discriminator Loss: 1.0129... Generator Loss: 1.3208
Epoch 2/2... Discriminator Loss: 0.9582... Generator Loss: 1.3671
Epoch 2/2... Discriminator Loss: 1.0083... Generator Loss: 1.2433
Epoch 2/2... Discriminator Loss: 0.9212... Generator Loss: 1.2819
Epoch 2/2... Discriminator Loss: 1.0308... Generator Loss: 1.0033
Epoch 2/2... Discriminator Loss: 0.9881... Generator Loss: 1.2540
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-33-a53d14203afe> in <module>()
     19 with tf.Graph().as_default():
     20     train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
---> 21           mnist_dataset.shape, mnist_dataset.image_mode)

/home/carnd/anaconda3/envs/dl/lib/python3.5/contextlib.py in __exit__(self, type, value, traceback)
     75                 value = type()
     76             try:
---> 77                 self.gen.throw(type, value, traceback)
     78                 raise RuntimeError("generator didn't stop after throw()")
     79             except StopIteration as exc:

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in get_controller(self, default)
   3679     try:
   3680       self.stack.append(default)
-> 3681       yield default
   3682     finally:
   3683       if self._enforce_nesting:

<ipython-input-33-a53d14203afe> in <module>()
     19 with tf.Graph().as_default():
     20     train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
---> 21           mnist_dataset.shape, mnist_dataset.image_mode)

<ipython-input-32-643231b50590> in train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode)
     65 #         pkl.dump(samples, f)
     66 
---> 67     show_generator_output(sess, 20, input_z, data_shape[3], data_image_mode)
     68 
     69     return losses, samples

<ipython-input-29-73239a44c0dc> in show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode)
     19     samples = sess.run(
     20         generator(input_z, out_channel_dim, False),
---> 21         feed_dict={input_z: example_z})
     22 
     23     images_grid = helper.images_square_grid(samples, image_mode)

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    765     try:
    766       result = self._run(None, fetches, feed_dict, options_ptr,
--> 767                          run_metadata_ptr)
    768       if run_metadata:
    769         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    901     # Check session.
    902     if self._closed:
--> 903       raise RuntimeError('Attempted to use a closed Session.')
    904     if self.graph.version == 0:
    905       raise RuntimeError('The Session graph is empty.  Add operations to the '

RuntimeError: Attempted to use a closed Session.

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [34]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5

print_every = 5
show_every = 20


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 0.5115... Generator Loss: 2.3266
Epoch 1/1... Discriminator Loss: 0.5042... Generator Loss: 2.9214
Epoch 1/1... Discriminator Loss: 0.5263... Generator Loss: 2.6969
Epoch 1/1... Discriminator Loss: 0.6903... Generator Loss: 2.1411
Epoch 1/1... Discriminator Loss: 0.5821... Generator Loss: 1.9652
Epoch 1/1... Discriminator Loss: 0.6648... Generator Loss: 3.7728
Epoch 1/1... Discriminator Loss: 0.6225... Generator Loss: 1.7895
Epoch 1/1... Discriminator Loss: 1.1006... Generator Loss: 1.3659
Epoch 1/1... Discriminator Loss: 0.7248... Generator Loss: 1.6401
Epoch 1/1... Discriminator Loss: 0.6583... Generator Loss: 2.3241
Epoch 1/1... Discriminator Loss: 0.6896... Generator Loss: 2.1070
Epoch 1/1... Discriminator Loss: 0.4795... Generator Loss: 3.4652
Epoch 1/1... Discriminator Loss: 0.5638... Generator Loss: 3.1492
Epoch 1/1... Discriminator Loss: 0.6184... Generator Loss: 2.4958
Epoch 1/1... Discriminator Loss: 0.4953... Generator Loss: 2.7527
Epoch 1/1... Discriminator Loss: 0.4927... Generator Loss: 2.9522
Epoch 1/1... Discriminator Loss: 0.4389... Generator Loss: 3.3173
Epoch 1/1... Discriminator Loss: 0.5934... Generator Loss: 2.5254
Epoch 1/1... Discriminator Loss: 0.4498... Generator Loss: 3.7576
Epoch 1/1... Discriminator Loss: 0.4610... Generator Loss: 3.5509
Epoch 1/1... Discriminator Loss: 0.4697... Generator Loss: 3.0088
Epoch 1/1... Discriminator Loss: 1.5132... Generator Loss: 0.7608
Epoch 1/1... Discriminator Loss: 0.4443... Generator Loss: 4.4216
Epoch 1/1... Discriminator Loss: 0.3970... Generator Loss: 3.6058
Epoch 1/1... Discriminator Loss: 0.4920... Generator Loss: 3.7623
Epoch 1/1... Discriminator Loss: 0.5043... Generator Loss: 2.4404
Epoch 1/1... Discriminator Loss: 0.4480... Generator Loss: 3.5040
Epoch 1/1... Discriminator Loss: 0.5089... Generator Loss: 4.2856
Epoch 1/1... Discriminator Loss: 0.5532... Generator Loss: 2.6661
Epoch 1/1... Discriminator Loss: 0.4483... Generator Loss: 2.9389
Epoch 1/1... Discriminator Loss: 0.5874... Generator Loss: 3.0143
Epoch 1/1... Discriminator Loss: 0.4790... Generator Loss: 3.7814
Epoch 1/1... Discriminator Loss: 0.7531... Generator Loss: 1.3370
Epoch 1/1... Discriminator Loss: 0.5302... Generator Loss: 2.9462
Epoch 1/1... Discriminator Loss: 0.4112... Generator Loss: 4.0722
Epoch 1/1... Discriminator Loss: 0.5051... Generator Loss: 3.3317
Epoch 1/1... Discriminator Loss: 0.6955... Generator Loss: 1.8856
Epoch 1/1... Discriminator Loss: 0.6470... Generator Loss: 1.9649
Epoch 1/1... Discriminator Loss: 0.5542... Generator Loss: 2.9299
Epoch 1/1... Discriminator Loss: 0.6758... Generator Loss: 2.5580
Epoch 1/1... Discriminator Loss: 0.5554... Generator Loss: 1.9428
Epoch 1/1... Discriminator Loss: 0.5315... Generator Loss: 2.7161
Epoch 1/1... Discriminator Loss: 1.3617... Generator Loss: 4.6338
Epoch 1/1... Discriminator Loss: 0.7577... Generator Loss: 1.6460
Epoch 1/1... Discriminator Loss: 1.0322... Generator Loss: 1.1011
Epoch 1/1... Discriminator Loss: 0.7891... Generator Loss: 1.4679
Epoch 1/1... Discriminator Loss: 0.7505... Generator Loss: 1.5212
Epoch 1/1... Discriminator Loss: 1.2536... Generator Loss: 0.7677
Epoch 1/1... Discriminator Loss: 1.0157... Generator Loss: 1.0926
Epoch 1/1... Discriminator Loss: 0.7060... Generator Loss: 1.9066
Epoch 1/1... Discriminator Loss: 0.9242... Generator Loss: 4.0297
Epoch 1/1... Discriminator Loss: 0.8110... Generator Loss: 1.3009
Epoch 1/1... Discriminator Loss: 1.6891... Generator Loss: 0.6908
Epoch 1/1... Discriminator Loss: 1.4459... Generator Loss: 0.7413
Epoch 1/1... Discriminator Loss: 1.6433... Generator Loss: 0.4216
Epoch 1/1... Discriminator Loss: 0.8436... Generator Loss: 2.2969
Epoch 1/1... Discriminator Loss: 1.0965... Generator Loss: 1.1442
Epoch 1/1... Discriminator Loss: 1.0373... Generator Loss: 2.4142
Epoch 1/1... Discriminator Loss: 1.0558... Generator Loss: 1.0319
Epoch 1/1... Discriminator Loss: 1.5973... Generator Loss: 0.6860
Epoch 1/1... Discriminator Loss: 1.0161... Generator Loss: 0.9902
Epoch 1/1... Discriminator Loss: 1.5192... Generator Loss: 3.6905
Epoch 1/1... Discriminator Loss: 1.9910... Generator Loss: 0.2924
Epoch 1/1... Discriminator Loss: 0.9866... Generator Loss: 1.2745
Epoch 1/1... Discriminator Loss: 1.2772... Generator Loss: 1.0166
Epoch 1/1... Discriminator Loss: 0.9962... Generator Loss: 1.4174
Epoch 1/1... Discriminator Loss: 0.8432... Generator Loss: 1.6557
Epoch 1/1... Discriminator Loss: 0.9756... Generator Loss: 1.4709
Epoch 1/1... Discriminator Loss: 1.1474... Generator Loss: 1.3625
Epoch 1/1... Discriminator Loss: 1.2949... Generator Loss: 1.4938
Epoch 1/1... Discriminator Loss: 1.0781... Generator Loss: 1.7280
Epoch 1/1... Discriminator Loss: 1.0690... Generator Loss: 1.3875
Epoch 1/1... Discriminator Loss: 0.9576... Generator Loss: 3.1145
Epoch 1/1... Discriminator Loss: 0.9725... Generator Loss: 1.5900
Epoch 1/1... Discriminator Loss: 1.4868... Generator Loss: 0.7935
Epoch 1/1... Discriminator Loss: 0.8882... Generator Loss: 1.5674
Epoch 1/1... Discriminator Loss: 0.8880... Generator Loss: 1.4221
Epoch 1/1... Discriminator Loss: 1.0613... Generator Loss: 1.1514
Epoch 1/1... Discriminator Loss: 1.4850... Generator Loss: 0.7051
Epoch 1/1... Discriminator Loss: 1.1718... Generator Loss: 2.0344
Epoch 1/1... Discriminator Loss: 0.9984... Generator Loss: 1.6516
Epoch 1/1... Discriminator Loss: 1.3068... Generator Loss: 0.8656
Epoch 1/1... Discriminator Loss: 1.1827... Generator Loss: 0.7822
Epoch 1/1... Discriminator Loss: 1.5255... Generator Loss: 0.7790
Epoch 1/1... Discriminator Loss: 0.8281... Generator Loss: 1.5985
Epoch 1/1... Discriminator Loss: 1.1799... Generator Loss: 1.1323
Epoch 1/1... Discriminator Loss: 1.1726... Generator Loss: 1.0653
Epoch 1/1... Discriminator Loss: 1.0572... Generator Loss: 1.2826
Epoch 1/1... Discriminator Loss: 1.2724... Generator Loss: 1.0394
Epoch 1/1... Discriminator Loss: 0.9691... Generator Loss: 1.3375
Epoch 1/1... Discriminator Loss: 1.0869... Generator Loss: 1.1273
Epoch 1/1... Discriminator Loss: 1.1291... Generator Loss: 1.2755
Epoch 1/1... Discriminator Loss: 1.0179... Generator Loss: 1.2286
Epoch 1/1... Discriminator Loss: 0.8552... Generator Loss: 1.8396
Epoch 1/1... Discriminator Loss: 1.1899... Generator Loss: 1.6295
Epoch 1/1... Discriminator Loss: 2.2219... Generator Loss: 0.3187
Epoch 1/1... Discriminator Loss: 1.3009... Generator Loss: 0.7739
Epoch 1/1... Discriminator Loss: 0.9810... Generator Loss: 1.4322
Epoch 1/1... Discriminator Loss: 1.2789... Generator Loss: 1.2850
Epoch 1/1... Discriminator Loss: 1.2783... Generator Loss: 1.1532
Epoch 1/1... Discriminator Loss: 1.1017... Generator Loss: 1.1827
Epoch 1/1... Discriminator Loss: 1.4640... Generator Loss: 0.6569
Epoch 1/1... Discriminator Loss: 1.1150... Generator Loss: 1.0260
Epoch 1/1... Discriminator Loss: 1.2570... Generator Loss: 0.8633
Epoch 1/1... Discriminator Loss: 1.0603... Generator Loss: 1.5566
Epoch 1/1... Discriminator Loss: 1.6838... Generator Loss: 0.5648
Epoch 1/1... Discriminator Loss: 1.5919... Generator Loss: 1.2367
Epoch 1/1... Discriminator Loss: 1.2040... Generator Loss: 1.0276
Epoch 1/1... Discriminator Loss: 1.2677... Generator Loss: 0.8976
Epoch 1/1... Discriminator Loss: 1.1158... Generator Loss: 0.8705
Epoch 1/1... Discriminator Loss: 1.0353... Generator Loss: 1.3756
Epoch 1/1... Discriminator Loss: 1.0129... Generator Loss: 1.2207
Epoch 1/1... Discriminator Loss: 1.1989... Generator Loss: 0.8280
Epoch 1/1... Discriminator Loss: 1.2369... Generator Loss: 0.8459
Epoch 1/1... Discriminator Loss: 1.2923... Generator Loss: 0.8761
Epoch 1/1... Discriminator Loss: 1.1521... Generator Loss: 0.9474
Epoch 1/1... Discriminator Loss: 1.2023... Generator Loss: 1.0299
Epoch 1/1... Discriminator Loss: 1.4173... Generator Loss: 0.7949
Epoch 1/1... Discriminator Loss: 1.3063... Generator Loss: 0.7507
Epoch 1/1... Discriminator Loss: 1.3462... Generator Loss: 1.0880
Epoch 1/1... Discriminator Loss: 1.3467... Generator Loss: 1.3257
Epoch 1/1... Discriminator Loss: 1.1385... Generator Loss: 0.8732
Epoch 1/1... Discriminator Loss: 1.5198... Generator Loss: 0.8404
Epoch 1/1... Discriminator Loss: 1.4422... Generator Loss: 0.9947
Epoch 1/1... Discriminator Loss: 1.4664... Generator Loss: 0.6811
Epoch 1/1... Discriminator Loss: 1.1754... Generator Loss: 0.9549
Epoch 1/1... Discriminator Loss: 1.2864... Generator Loss: 0.8117
Epoch 1/1... Discriminator Loss: 1.2662... Generator Loss: 0.8656
Epoch 1/1... Discriminator Loss: 1.4110... Generator Loss: 1.0940
Epoch 1/1... Discriminator Loss: 1.3326... Generator Loss: 0.9980
Epoch 1/1... Discriminator Loss: 1.2282... Generator Loss: 0.6959
Epoch 1/1... Discriminator Loss: 1.1854... Generator Loss: 1.0500
Epoch 1/1... Discriminator Loss: 1.6282... Generator Loss: 1.1381
Epoch 1/1... Discriminator Loss: 1.5565... Generator Loss: 0.7677
Epoch 1/1... Discriminator Loss: 1.3521... Generator Loss: 1.0407
Epoch 1/1... Discriminator Loss: 1.2944... Generator Loss: 0.8213
Epoch 1/1... Discriminator Loss: 1.0164... Generator Loss: 0.9821
Epoch 1/1... Discriminator Loss: 1.4710... Generator Loss: 0.8601
Epoch 1/1... Discriminator Loss: 1.2244... Generator Loss: 1.1559
Epoch 1/1... Discriminator Loss: 1.2823... Generator Loss: 1.0956
Epoch 1/1... Discriminator Loss: 1.5344... Generator Loss: 0.6541
Epoch 1/1... Discriminator Loss: 1.0589... Generator Loss: 1.2315
Epoch 1/1... Discriminator Loss: 1.2833... Generator Loss: 1.0034
Epoch 1/1... Discriminator Loss: 1.2660... Generator Loss: 1.0244
Epoch 1/1... Discriminator Loss: 1.5188... Generator Loss: 0.9616
Epoch 1/1... Discriminator Loss: 1.5301... Generator Loss: 0.6552
Epoch 1/1... Discriminator Loss: 1.0620... Generator Loss: 1.0982
Epoch 1/1... Discriminator Loss: 1.2646... Generator Loss: 0.9403
Epoch 1/1... Discriminator Loss: 1.2526... Generator Loss: 1.1792
Epoch 1/1... Discriminator Loss: 1.4000... Generator Loss: 0.7441
Epoch 1/1... Discriminator Loss: 1.2736... Generator Loss: 1.0655
Epoch 1/1... Discriminator Loss: 1.3795... Generator Loss: 0.9373
Epoch 1/1... Discriminator Loss: 1.1503... Generator Loss: 0.9468
Epoch 1/1... Discriminator Loss: 1.3333... Generator Loss: 0.8811
Epoch 1/1... Discriminator Loss: 1.4774... Generator Loss: 0.8441
Epoch 1/1... Discriminator Loss: 1.2379... Generator Loss: 0.8245
Epoch 1/1... Discriminator Loss: 1.6978... Generator Loss: 0.7575
Epoch 1/1... Discriminator Loss: 1.1975... Generator Loss: 0.8612
Epoch 1/1... Discriminator Loss: 1.3317... Generator Loss: 0.7637
Epoch 1/1... Discriminator Loss: 1.3361... Generator Loss: 0.9580
Epoch 1/1... Discriminator Loss: 1.4854... Generator Loss: 0.8843
Epoch 1/1... Discriminator Loss: 1.3603... Generator Loss: 0.7090
Epoch 1/1... Discriminator Loss: 1.2994... Generator Loss: 0.8296
Epoch 1/1... Discriminator Loss: 1.1373... Generator Loss: 0.8860
Epoch 1/1... Discriminator Loss: 1.3472... Generator Loss: 0.7636
Epoch 1/1... Discriminator Loss: 1.3398... Generator Loss: 0.8149
Epoch 1/1... Discriminator Loss: 1.1900... Generator Loss: 0.9943
Epoch 1/1... Discriminator Loss: 1.2435... Generator Loss: 1.0257
Epoch 1/1... Discriminator Loss: 1.2551... Generator Loss: 0.8391
Epoch 1/1... Discriminator Loss: 1.2253... Generator Loss: 0.8218
Epoch 1/1... Discriminator Loss: 1.2961... Generator Loss: 0.9079
Epoch 1/1... Discriminator Loss: 1.4252... Generator Loss: 0.8607
Epoch 1/1... Discriminator Loss: 1.1804... Generator Loss: 1.0630
Epoch 1/1... Discriminator Loss: 1.5876... Generator Loss: 0.7724
Epoch 1/1... Discriminator Loss: 1.4059... Generator Loss: 0.7881
Epoch 1/1... Discriminator Loss: 1.2244... Generator Loss: 0.9483
Epoch 1/1... Discriminator Loss: 1.2452... Generator Loss: 0.7979
Epoch 1/1... Discriminator Loss: 1.3403... Generator Loss: 0.9777
Epoch 1/1... Discriminator Loss: 1.4293... Generator Loss: 0.7935
Epoch 1/1... Discriminator Loss: 1.1715... Generator Loss: 1.1539
Epoch 1/1... Discriminator Loss: 1.2525... Generator Loss: 0.8451
Epoch 1/1... Discriminator Loss: 1.3048... Generator Loss: 0.9147
Epoch 1/1... Discriminator Loss: 1.2046... Generator Loss: 0.9815
Epoch 1/1... Discriminator Loss: 1.3883... Generator Loss: 0.8117
Epoch 1/1... Discriminator Loss: 1.2637... Generator Loss: 0.9474
Epoch 1/1... Discriminator Loss: 1.3276... Generator Loss: 0.9986
Epoch 1/1... Discriminator Loss: 1.2474... Generator Loss: 0.8542
Epoch 1/1... Discriminator Loss: 1.4305... Generator Loss: 0.6264
Epoch 1/1... Discriminator Loss: 1.2758... Generator Loss: 1.0630
Epoch 1/1... Discriminator Loss: 1.3112... Generator Loss: 0.8268
Epoch 1/1... Discriminator Loss: 1.4629... Generator Loss: 0.7509
Epoch 1/1... Discriminator Loss: 1.3309... Generator Loss: 0.9960
Epoch 1/1... Discriminator Loss: 1.2722... Generator Loss: 0.9302
Epoch 1/1... Discriminator Loss: 1.3110... Generator Loss: 0.8746
Epoch 1/1... Discriminator Loss: 1.4411... Generator Loss: 0.8410
Epoch 1/1... Discriminator Loss: 1.3571... Generator Loss: 0.9165
Epoch 1/1... Discriminator Loss: 1.3379... Generator Loss: 0.8796
Epoch 1/1... Discriminator Loss: 1.2605... Generator Loss: 0.9969
Epoch 1/1... Discriminator Loss: 1.3921... Generator Loss: 0.8491
Epoch 1/1... Discriminator Loss: 1.2764... Generator Loss: 0.9624
Epoch 1/1... Discriminator Loss: 1.2653... Generator Loss: 0.7298
Epoch 1/1... Discriminator Loss: 1.3285... Generator Loss: 0.8554
Epoch 1/1... Discriminator Loss: 1.4406... Generator Loss: 0.7607
Epoch 1/1... Discriminator Loss: 1.2659... Generator Loss: 0.9982
Epoch 1/1... Discriminator Loss: 1.1806... Generator Loss: 0.9018
Epoch 1/1... Discriminator Loss: 1.2443... Generator Loss: 0.8731
Epoch 1/1... Discriminator Loss: 1.3558... Generator Loss: 0.9205
Epoch 1/1... Discriminator Loss: 1.2819... Generator Loss: 0.8854
Epoch 1/1... Discriminator Loss: 1.3708... Generator Loss: 0.7414
Epoch 1/1... Discriminator Loss: 1.2989... Generator Loss: 0.8963
Epoch 1/1... Discriminator Loss: 1.1760... Generator Loss: 0.9279
Epoch 1/1... Discriminator Loss: 1.2655... Generator Loss: 0.9314
Epoch 1/1... Discriminator Loss: 1.4279... Generator Loss: 0.8164
Epoch 1/1... Discriminator Loss: 1.4240... Generator Loss: 0.6789
Epoch 1/1... Discriminator Loss: 1.2416... Generator Loss: 0.8500
Epoch 1/1... Discriminator Loss: 1.1546... Generator Loss: 0.8839
Epoch 1/1... Discriminator Loss: 1.4039... Generator Loss: 0.7673
Epoch 1/1... Discriminator Loss: 1.3615... Generator Loss: 0.6932
Epoch 1/1... Discriminator Loss: 1.2866... Generator Loss: 0.8877
Epoch 1/1... Discriminator Loss: 1.2284... Generator Loss: 0.7567
Epoch 1/1... Discriminator Loss: 1.2544... Generator Loss: 0.8516
Epoch 1/1... Discriminator Loss: 1.3647... Generator Loss: 0.7626
Epoch 1/1... Discriminator Loss: 1.2505... Generator Loss: 0.8542
Epoch 1/1... Discriminator Loss: 1.4034... Generator Loss: 0.7887
Epoch 1/1... Discriminator Loss: 1.2545... Generator Loss: 1.0395
Epoch 1/1... Discriminator Loss: 1.1555... Generator Loss: 0.9950
Epoch 1/1... Discriminator Loss: 1.3801... Generator Loss: 0.8476
Epoch 1/1... Discriminator Loss: 1.3228... Generator Loss: 0.9207
Epoch 1/1... Discriminator Loss: 1.3819... Generator Loss: 0.9142
Epoch 1/1... Discriminator Loss: 1.2701... Generator Loss: 0.6811
Epoch 1/1... Discriminator Loss: 1.2275... Generator Loss: 0.7725
Epoch 1/1... Discriminator Loss: 1.2172... Generator Loss: 0.8196
Epoch 1/1... Discriminator Loss: 1.2487... Generator Loss: 1.0388
Epoch 1/1... Discriminator Loss: 1.2104... Generator Loss: 0.8947
Epoch 1/1... Discriminator Loss: 1.3114... Generator Loss: 0.9820
Epoch 1/1... Discriminator Loss: 1.3868... Generator Loss: 0.7703
Epoch 1/1... Discriminator Loss: 1.2654... Generator Loss: 0.9365
Epoch 1/1... Discriminator Loss: 1.3621... Generator Loss: 0.8351
Epoch 1/1... Discriminator Loss: 1.4491... Generator Loss: 0.7629
Epoch 1/1... Discriminator Loss: 1.2673... Generator Loss: 0.7552
Epoch 1/1... Discriminator Loss: 1.0993... Generator Loss: 1.1215
Epoch 1/1... Discriminator Loss: 1.2945... Generator Loss: 0.8715
Epoch 1/1... Discriminator Loss: 1.3238... Generator Loss: 0.6694
Epoch 1/1... Discriminator Loss: 1.4568... Generator Loss: 0.7351
Epoch 1/1... Discriminator Loss: 1.1933... Generator Loss: 0.8757
Epoch 1/1... Discriminator Loss: 1.6028... Generator Loss: 0.6536
Epoch 1/1... Discriminator Loss: 1.3476... Generator Loss: 0.7331
Epoch 1/1... Discriminator Loss: 1.3221... Generator Loss: 0.9446
Epoch 1/1... Discriminator Loss: 1.3052... Generator Loss: 0.8987
Epoch 1/1... Discriminator Loss: 1.4868... Generator Loss: 0.5948
Epoch 1/1... Discriminator Loss: 1.2928... Generator Loss: 0.8051
Epoch 1/1... Discriminator Loss: 1.2764... Generator Loss: 0.9599
Epoch 1/1... Discriminator Loss: 1.2365... Generator Loss: 0.9943
Epoch 1/1... Discriminator Loss: 1.3700... Generator Loss: 0.8618
Epoch 1/1... Discriminator Loss: 1.2319... Generator Loss: 0.8585
Epoch 1/1... Discriminator Loss: 1.3258... Generator Loss: 0.8567
Epoch 1/1... Discriminator Loss: 1.2428... Generator Loss: 0.8187
Epoch 1/1... Discriminator Loss: 1.2993... Generator Loss: 0.8979
Epoch 1/1... Discriminator Loss: 1.2359... Generator Loss: 0.8734
Epoch 1/1... Discriminator Loss: 1.3742... Generator Loss: 0.9641
Epoch 1/1... Discriminator Loss: 1.3643... Generator Loss: 0.8318
Epoch 1/1... Discriminator Loss: 1.3167... Generator Loss: 0.8478
Epoch 1/1... Discriminator Loss: 1.4031... Generator Loss: 0.7161
Epoch 1/1... Discriminator Loss: 1.2360... Generator Loss: 0.9768
Epoch 1/1... Discriminator Loss: 1.2701... Generator Loss: 0.8159
Epoch 1/1... Discriminator Loss: 1.3278... Generator Loss: 0.8426
Epoch 1/1... Discriminator Loss: 1.2191... Generator Loss: 0.7785
Epoch 1/1... Discriminator Loss: 1.1884... Generator Loss: 0.8678
Epoch 1/1... Discriminator Loss: 1.3264... Generator Loss: 0.8777
Epoch 1/1... Discriminator Loss: 1.2849... Generator Loss: 0.7987
Epoch 1/1... Discriminator Loss: 1.4134... Generator Loss: 0.8521
Epoch 1/1... Discriminator Loss: 1.4313... Generator Loss: 0.7861
Epoch 1/1... Discriminator Loss: 1.3385... Generator Loss: 0.7582
Epoch 1/1... Discriminator Loss: 1.1645... Generator Loss: 0.9067
Epoch 1/1... Discriminator Loss: 1.2851... Generator Loss: 0.8379
Epoch 1/1... Discriminator Loss: 1.3425... Generator Loss: 0.7344
Epoch 1/1... Discriminator Loss: 1.3601... Generator Loss: 0.7136
Epoch 1/1... Discriminator Loss: 1.3072... Generator Loss: 0.7189
Epoch 1/1... Discriminator Loss: 1.3323... Generator Loss: 1.0534
Epoch 1/1... Discriminator Loss: 1.3374... Generator Loss: 0.6992
Epoch 1/1... Discriminator Loss: 1.3089... Generator Loss: 0.9533
Epoch 1/1... Discriminator Loss: 1.2527... Generator Loss: 1.0182
Epoch 1/1... Discriminator Loss: 1.3379... Generator Loss: 0.7980
Epoch 1/1... Discriminator Loss: 1.3118... Generator Loss: 0.8403
Epoch 1/1... Discriminator Loss: 1.4011... Generator Loss: 0.8459
Epoch 1/1... Discriminator Loss: 1.3970... Generator Loss: 0.7156
Epoch 1/1... Discriminator Loss: 1.3198... Generator Loss: 0.7404
Epoch 1/1... Discriminator Loss: 1.4183... Generator Loss: 0.8650
Epoch 1/1... Discriminator Loss: 1.2168... Generator Loss: 0.8925
Epoch 1/1... Discriminator Loss: 1.4818... Generator Loss: 0.7060
Epoch 1/1... Discriminator Loss: 1.3807... Generator Loss: 0.7928
Epoch 1/1... Discriminator Loss: 1.3416... Generator Loss: 0.7569
Epoch 1/1... Discriminator Loss: 1.4315... Generator Loss: 0.7569
Epoch 1/1... Discriminator Loss: 1.3707... Generator Loss: 0.7778
Epoch 1/1... Discriminator Loss: 1.3789... Generator Loss: 0.8243
Epoch 1/1... Discriminator Loss: 1.2523... Generator Loss: 0.8579
Epoch 1/1... Discriminator Loss: 1.2922... Generator Loss: 0.8296
Epoch 1/1... Discriminator Loss: 1.3523... Generator Loss: 0.9075
Epoch 1/1... Discriminator Loss: 1.3741... Generator Loss: 0.9200
Epoch 1/1... Discriminator Loss: 1.3739... Generator Loss: 0.7415
Epoch 1/1... Discriminator Loss: 1.4064... Generator Loss: 0.8152
Epoch 1/1... Discriminator Loss: 1.3866... Generator Loss: 0.8549
Epoch 1/1... Discriminator Loss: 1.2473... Generator Loss: 0.8688
Epoch 1/1... Discriminator Loss: 1.2584... Generator Loss: 0.8695
Epoch 1/1... Discriminator Loss: 1.3216... Generator Loss: 0.7748
Epoch 1/1... Discriminator Loss: 1.3351... Generator Loss: 0.7380
Epoch 1/1... Discriminator Loss: 1.2423... Generator Loss: 0.8412
Epoch 1/1... Discriminator Loss: 1.1790... Generator Loss: 0.9260
Epoch 1/1... Discriminator Loss: 1.2342... Generator Loss: 0.9106
Epoch 1/1... Discriminator Loss: 1.1833... Generator Loss: 0.8688
Epoch 1/1... Discriminator Loss: 1.3805... Generator Loss: 0.8538
Epoch 1/1... Discriminator Loss: 1.3237... Generator Loss: 0.7365
Epoch 1/1... Discriminator Loss: 1.2715... Generator Loss: 0.8838
Epoch 1/1... Discriminator Loss: 1.3490... Generator Loss: 0.8419
Epoch 1/1... Discriminator Loss: 1.1583... Generator Loss: 0.8968
Epoch 1/1... Discriminator Loss: 1.2528... Generator Loss: 0.7486
Epoch 1/1... Discriminator Loss: 1.5120... Generator Loss: 0.7106
Epoch 1/1... Discriminator Loss: 1.2770... Generator Loss: 0.9271
Epoch 1/1... Discriminator Loss: 1.4246... Generator Loss: 0.9168
Epoch 1/1... Discriminator Loss: 1.3302... Generator Loss: 0.9239
Epoch 1/1... Discriminator Loss: 1.3651... Generator Loss: 0.7861
Epoch 1/1... Discriminator Loss: 1.2674... Generator Loss: 0.8594
Epoch 1/1... Discriminator Loss: 1.3294... Generator Loss: 0.8491
Epoch 1/1... Discriminator Loss: 1.1891... Generator Loss: 0.8842
Epoch 1/1... Discriminator Loss: 1.1925... Generator Loss: 0.8753
Epoch 1/1... Discriminator Loss: 1.2028... Generator Loss: 0.8846
Epoch 1/1... Discriminator Loss: 1.2814... Generator Loss: 0.8818
Epoch 1/1... Discriminator Loss: 1.2974... Generator Loss: 0.7826
Epoch 1/1... Discriminator Loss: 1.2147... Generator Loss: 0.8919
Epoch 1/1... Discriminator Loss: 1.3494... Generator Loss: 0.8194
Epoch 1/1... Discriminator Loss: 1.3918... Generator Loss: 0.7473
Epoch 1/1... Discriminator Loss: 1.3595... Generator Loss: 0.8092
Epoch 1/1... Discriminator Loss: 1.5034... Generator Loss: 0.8370
Epoch 1/1... Discriminator Loss: 1.3148... Generator Loss: 0.8946
Epoch 1/1... Discriminator Loss: 1.3492... Generator Loss: 0.7119
Epoch 1/1... Discriminator Loss: 1.3761... Generator Loss: 0.8753
Epoch 1/1... Discriminator Loss: 1.3003... Generator Loss: 0.8392
Epoch 1/1... Discriminator Loss: 1.2914... Generator Loss: 0.8217
Epoch 1/1... Discriminator Loss: 1.3193... Generator Loss: 0.8281
Epoch 1/1... Discriminator Loss: 1.3424... Generator Loss: 0.8050
Epoch 1/1... Discriminator Loss: 1.3223... Generator Loss: 0.8033
Epoch 1/1... Discriminator Loss: 1.1928... Generator Loss: 0.9397
Epoch 1/1... Discriminator Loss: 1.3612... Generator Loss: 0.7376
Epoch 1/1... Discriminator Loss: 1.3529... Generator Loss: 0.8502
Epoch 1/1... Discriminator Loss: 1.3439... Generator Loss: 0.8046
Epoch 1/1... Discriminator Loss: 1.2842... Generator Loss: 1.0291
Epoch 1/1... Discriminator Loss: 1.2586... Generator Loss: 0.8466
Epoch 1/1... Discriminator Loss: 1.3516... Generator Loss: 0.8192
Epoch 1/1... Discriminator Loss: 1.3053... Generator Loss: 0.8903
Epoch 1/1... Discriminator Loss: 1.2250... Generator Loss: 0.9381
Epoch 1/1... Discriminator Loss: 1.2837... Generator Loss: 0.9059
Epoch 1/1... Discriminator Loss: 1.2326... Generator Loss: 0.9064
Epoch 1/1... Discriminator Loss: 1.3532... Generator Loss: 0.7469
Epoch 1/1... Discriminator Loss: 1.3210... Generator Loss: 0.8353
Epoch 1/1... Discriminator Loss: 1.3869... Generator Loss: 0.7530
Epoch 1/1... Discriminator Loss: 1.2888... Generator Loss: 0.8501
Epoch 1/1... Discriminator Loss: 1.3138... Generator Loss: 0.7294
Epoch 1/1... Discriminator Loss: 1.2678... Generator Loss: 0.8827
Epoch 1/1... Discriminator Loss: 1.2859... Generator Loss: 0.9284
Epoch 1/1... Discriminator Loss: 1.1818... Generator Loss: 0.9161
Epoch 1/1... Discriminator Loss: 1.2270... Generator Loss: 0.9642
Epoch 1/1... Discriminator Loss: 1.4148... Generator Loss: 0.7667
Epoch 1/1... Discriminator Loss: 1.1589... Generator Loss: 1.0720
Epoch 1/1... Discriminator Loss: 1.2267... Generator Loss: 0.8978
Epoch 1/1... Discriminator Loss: 1.2861... Generator Loss: 0.9369
Epoch 1/1... Discriminator Loss: 1.4252... Generator Loss: 0.7408
Epoch 1/1... Discriminator Loss: 1.4603... Generator Loss: 0.7078
Epoch 1/1... Discriminator Loss: 1.2641... Generator Loss: 0.8554
Epoch 1/1... Discriminator Loss: 1.4575... Generator Loss: 0.6839
Epoch 1/1... Discriminator Loss: 1.2788... Generator Loss: 0.8537
Epoch 1/1... Discriminator Loss: 1.3688... Generator Loss: 0.7470
Epoch 1/1... Discriminator Loss: 1.3626... Generator Loss: 0.7264
Epoch 1/1... Discriminator Loss: 1.2743... Generator Loss: 0.8544
Epoch 1/1... Discriminator Loss: 1.5237... Generator Loss: 0.6652
Epoch 1/1... Discriminator Loss: 1.3631... Generator Loss: 0.7610
Epoch 1/1... Discriminator Loss: 1.3389... Generator Loss: 0.8837
Epoch 1/1... Discriminator Loss: 1.2257... Generator Loss: 0.8112
Epoch 1/1... Discriminator Loss: 1.2390... Generator Loss: 0.7218
Epoch 1/1... Discriminator Loss: 1.4571... Generator Loss: 0.7005
Epoch 1/1... Discriminator Loss: 1.3416... Generator Loss: 0.7363
Epoch 1/1... Discriminator Loss: 1.2850... Generator Loss: 0.6962
Epoch 1/1... Discriminator Loss: 1.4400... Generator Loss: 0.7194
Epoch 1/1... Discriminator Loss: 1.2990... Generator Loss: 0.8658
Epoch 1/1... Discriminator Loss: 1.2463... Generator Loss: 0.8626
Epoch 1/1... Discriminator Loss: 1.2612... Generator Loss: 0.8818
Epoch 1/1... Discriminator Loss: 1.3218... Generator Loss: 0.8923
Epoch 1/1... Discriminator Loss: 1.3415... Generator Loss: 0.8468
Epoch 1/1... Discriminator Loss: 1.3072... Generator Loss: 0.7822
Epoch 1/1... Discriminator Loss: 1.2489... Generator Loss: 0.8453
Epoch 1/1... Discriminator Loss: 1.2233... Generator Loss: 0.9595
Epoch 1/1... Discriminator Loss: 1.4212... Generator Loss: 0.5918
Epoch 1/1... Discriminator Loss: 1.3947... Generator Loss: 0.8288
Epoch 1/1... Discriminator Loss: 1.3444... Generator Loss: 0.7960
Epoch 1/1... Discriminator Loss: 1.2468... Generator Loss: 0.8349
Epoch 1/1... Discriminator Loss: 1.2743... Generator Loss: 0.8962
Epoch 1/1... Discriminator Loss: 1.3449... Generator Loss: 0.6952
Epoch 1/1... Discriminator Loss: 1.1642... Generator Loss: 0.9182
Epoch 1/1... Discriminator Loss: 1.2320... Generator Loss: 1.0115
Epoch 1/1... Discriminator Loss: 1.4015... Generator Loss: 0.6953
Epoch 1/1... Discriminator Loss: 1.2804... Generator Loss: 0.8388
Epoch 1/1... Discriminator Loss: 1.2382... Generator Loss: 0.8932
Epoch 1/1... Discriminator Loss: 1.3900... Generator Loss: 0.9158
Epoch 1/1... Discriminator Loss: 1.2921... Generator Loss: 0.8387
Epoch 1/1... Discriminator Loss: 1.3158... Generator Loss: 0.8749
Epoch 1/1... Discriminator Loss: 1.2928... Generator Loss: 0.9389
Epoch 1/1... Discriminator Loss: 1.0852... Generator Loss: 0.9587
Epoch 1/1... Discriminator Loss: 1.2231... Generator Loss: 0.8429
Epoch 1/1... Discriminator Loss: 1.3498... Generator Loss: 0.7759
Epoch 1/1... Discriminator Loss: 1.3334... Generator Loss: 0.7483
Epoch 1/1... Discriminator Loss: 1.4662... Generator Loss: 0.7659
Epoch 1/1... Discriminator Loss: 1.5202... Generator Loss: 0.6097
Epoch 1/1... Discriminator Loss: 1.3304... Generator Loss: 0.8389
Epoch 1/1... Discriminator Loss: 1.3731... Generator Loss: 0.7732
Epoch 1/1... Discriminator Loss: 1.2292... Generator Loss: 0.8534
Epoch 1/1... Discriminator Loss: 1.3310... Generator Loss: 0.9870
Epoch 1/1... Discriminator Loss: 1.2499... Generator Loss: 0.9564
Epoch 1/1... Discriminator Loss: 1.5006... Generator Loss: 0.5734
Epoch 1/1... Discriminator Loss: 1.3170... Generator Loss: 0.8118
Epoch 1/1... Discriminator Loss: 1.2935... Generator Loss: 0.8351
Epoch 1/1... Discriminator Loss: 1.3120... Generator Loss: 0.8174
Epoch 1/1... Discriminator Loss: 1.2266... Generator Loss: 0.8077
Epoch 1/1... Discriminator Loss: 1.4271... Generator Loss: 0.8013
Epoch 1/1... Discriminator Loss: 1.4277... Generator Loss: 0.7027
Epoch 1/1... Discriminator Loss: 1.4045... Generator Loss: 0.8571
Epoch 1/1... Discriminator Loss: 1.4132... Generator Loss: 0.6321
Epoch 1/1... Discriminator Loss: 1.3622... Generator Loss: 0.8073
Epoch 1/1... Discriminator Loss: 1.5297... Generator Loss: 0.6356
Epoch 1/1... Discriminator Loss: 1.3307... Generator Loss: 0.8729
Epoch 1/1... Discriminator Loss: 1.3305... Generator Loss: 0.7341
Epoch 1/1... Discriminator Loss: 1.2898... Generator Loss: 0.8694
Epoch 1/1... Discriminator Loss: 1.3623... Generator Loss: 0.8904
Epoch 1/1... Discriminator Loss: 1.2287... Generator Loss: 0.8518
Epoch 1/1... Discriminator Loss: 1.3025... Generator Loss: 0.8023
Epoch 1/1... Discriminator Loss: 1.2719... Generator Loss: 0.7998
Epoch 1/1... Discriminator Loss: 1.3476... Generator Loss: 0.7944
Epoch 1/1... Discriminator Loss: 1.3885... Generator Loss: 0.6363
Epoch 1/1... Discriminator Loss: 1.3862... Generator Loss: 0.7152
Epoch 1/1... Discriminator Loss: 1.3394... Generator Loss: 0.8891
Epoch 1/1... Discriminator Loss: 1.2358... Generator Loss: 0.9787
Epoch 1/1... Discriminator Loss: 1.2581... Generator Loss: 0.7523
Epoch 1/1... Discriminator Loss: 1.2348... Generator Loss: 0.7887
Epoch 1/1... Discriminator Loss: 1.4376... Generator Loss: 0.7814
Epoch 1/1... Discriminator Loss: 1.2466... Generator Loss: 0.8182
Epoch 1/1... Discriminator Loss: 1.3595... Generator Loss: 0.7712
Epoch 1/1... Discriminator Loss: 1.2644... Generator Loss: 0.8441
Epoch 1/1... Discriminator Loss: 1.2288... Generator Loss: 0.9264
Epoch 1/1... Discriminator Loss: 1.3554... Generator Loss: 0.8042
Epoch 1/1... Discriminator Loss: 1.2748... Generator Loss: 0.7664
Epoch 1/1... Discriminator Loss: 1.4557... Generator Loss: 0.7790
Epoch 1/1... Discriminator Loss: 1.3748... Generator Loss: 0.9369
Epoch 1/1... Discriminator Loss: 1.2398... Generator Loss: 0.7718
Epoch 1/1... Discriminator Loss: 1.3729... Generator Loss: 0.7360
Epoch 1/1... Discriminator Loss: 1.1684... Generator Loss: 0.8367
Epoch 1/1... Discriminator Loss: 1.3562... Generator Loss: 0.7671
Epoch 1/1... Discriminator Loss: 1.4447... Generator Loss: 0.8266
Epoch 1/1... Discriminator Loss: 1.2529... Generator Loss: 0.8425
Epoch 1/1... Discriminator Loss: 1.3672... Generator Loss: 0.7226
Epoch 1/1... Discriminator Loss: 1.4584... Generator Loss: 0.6978
Epoch 1/1... Discriminator Loss: 1.3491... Generator Loss: 0.6861
Epoch 1/1... Discriminator Loss: 1.2806... Generator Loss: 0.8599
Epoch 1/1... Discriminator Loss: 1.2143... Generator Loss: 0.8526
Epoch 1/1... Discriminator Loss: 1.4504... Generator Loss: 0.6468
Epoch 1/1... Discriminator Loss: 1.3802... Generator Loss: 0.8436
Epoch 1/1... Discriminator Loss: 1.3007... Generator Loss: 0.6978
Epoch 1/1... Discriminator Loss: 1.1681... Generator Loss: 0.9050
Epoch 1/1... Discriminator Loss: 1.2513... Generator Loss: 0.7876
Epoch 1/1... Discriminator Loss: 1.4006... Generator Loss: 0.8875
Epoch 1/1... Discriminator Loss: 1.3719... Generator Loss: 0.7978
Epoch 1/1... Discriminator Loss: 1.3769... Generator Loss: 0.7249
Epoch 1/1... Discriminator Loss: 1.1660... Generator Loss: 0.8736
Epoch 1/1... Discriminator Loss: 1.3482... Generator Loss: 0.8650
Epoch 1/1... Discriminator Loss: 1.1596... Generator Loss: 0.9463
Epoch 1/1... Discriminator Loss: 1.4654... Generator Loss: 0.7301
Epoch 1/1... Discriminator Loss: 1.4197... Generator Loss: 0.6675
Epoch 1/1... Discriminator Loss: 1.3579... Generator Loss: 0.8194
Epoch 1/1... Discriminator Loss: 1.5114... Generator Loss: 0.7163
Epoch 1/1... Discriminator Loss: 1.2769... Generator Loss: 0.8084
Epoch 1/1... Discriminator Loss: 1.4121... Generator Loss: 0.7938
Epoch 1/1... Discriminator Loss: 1.2435... Generator Loss: 0.8560
Epoch 1/1... Discriminator Loss: 1.2561... Generator Loss: 0.7822
Epoch 1/1... Discriminator Loss: 1.2818... Generator Loss: 0.7472
Epoch 1/1... Discriminator Loss: 1.3667... Generator Loss: 0.7390
Epoch 1/1... Discriminator Loss: 1.3933... Generator Loss: 0.7742
Epoch 1/1... Discriminator Loss: 1.3337... Generator Loss: 0.7427
Epoch 1/1... Discriminator Loss: 1.3268... Generator Loss: 0.8217
Epoch 1/1... Discriminator Loss: 1.5194... Generator Loss: 0.6241
Epoch 1/1... Discriminator Loss: 1.3262... Generator Loss: 0.9479
Epoch 1/1... Discriminator Loss: 1.4175... Generator Loss: 0.8205
Epoch 1/1... Discriminator Loss: 1.3138... Generator Loss: 0.8183
Epoch 1/1... Discriminator Loss: 1.2549... Generator Loss: 0.8055
Epoch 1/1... Discriminator Loss: 1.3646... Generator Loss: 0.7855
Epoch 1/1... Discriminator Loss: 1.3998... Generator Loss: 0.8347
Epoch 1/1... Discriminator Loss: 1.3225... Generator Loss: 0.7024
Epoch 1/1... Discriminator Loss: 1.4472... Generator Loss: 0.6488
Epoch 1/1... Discriminator Loss: 1.3812... Generator Loss: 0.7665
Epoch 1/1... Discriminator Loss: 1.3512... Generator Loss: 0.6852
Epoch 1/1... Discriminator Loss: 1.3268... Generator Loss: 0.7432
Epoch 1/1... Discriminator Loss: 1.4114... Generator Loss: 0.8173
Epoch 1/1... Discriminator Loss: 1.3572... Generator Loss: 0.6798
Epoch 1/1... Discriminator Loss: 1.1811... Generator Loss: 0.8731
Epoch 1/1... Discriminator Loss: 1.3467... Generator Loss: 0.6981
Epoch 1/1... Discriminator Loss: 1.3390... Generator Loss: 0.7434
Epoch 1/1... Discriminator Loss: 1.5751... Generator Loss: 0.6036
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-34-f1a80e10b79f> in <module>()
     16 with tf.Graph().as_default():
     17     train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
---> 18           celeba_dataset.shape, celeba_dataset.image_mode)

<ipython-input-32-643231b50590> in train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode)
     42 
     43                 # Run optimizers
---> 44                 _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
     45                 _ = sess.run(g_train_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})
     46                 _ = sess.run(g_train_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    765     try:
    766       result = self._run(None, fetches, feed_dict, options_ptr,
--> 767                          run_metadata_ptr)
    768       if run_metadata:
    769         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    963     if final_fetches or final_targets:
    964       results = self._do_run(handle, final_targets, final_fetches,
--> 965                              feed_dict_string, options, run_metadata)
    966     else:
    967       results = []

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1013     if handle is None:
   1014       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1015                            target_list, options, run_metadata)
   1016     else:
   1017       return self._do_call(_prun_fn, self._session, handle, feed_dict,

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1020   def _do_call(self, fn, *args):
   1021     try:
-> 1022       return fn(*args)
   1023     except errors.OpError as e:
   1024       message = compat.as_text(e.message)

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1002         return tf_session.TF_Run(session, options,
   1003                                  feed_dict, fetch_list, target_list,
-> 1004                                  status, run_metadata)
   1005 
   1006     def _prun_fn(session, handle, feed_dict, fetch_list):

KeyboardInterrupt: 

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.